Ubuntu16.04系统plot出现permission denied
import numpy as np  
from keras.models import Sequential  
from keras.layers.core import Dense, Activation  
from keras.optimizers import SGD  
from keras.utils import np_utils      
from keras.utils.visualize_util import plot  
def run():  
    model = Sequential()  
    model.add(Dense(4, input_dim=2, init='uniform'))  
    model.add(Activation('relu'))  
    model.add(Dense(2, init='uniform'))  
    model.add(Activation('sigmoid'))  
    sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)  
    model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])  
    plot(model, to_file='model.png')  
if __name__ == '__main__':  
    run()  

错误提示:
Traceback (most recent call last):
File "example2.py", line 21, in
run()

File "example2.py", line 18, in run
plot(model, to_file='model.png')

File "/home/c249/anaconda2/lib/python2.7/site-packages/keras/utils/visualize_util.py", line 64, in plot
dot.write_png(to_file)
File "/home/c249/anaconda2/lib/python2.7/site-packages/pydot.py", line 1811, in
lambda path, f=frmt, prog=self.prog : self.write(path, format=f, prog=prog))
File "/home/c249/anaconda2/lib/python2.7/site-packages/pydot.py", line 1913, in write
dot_fd.write(self.create(prog, format))
File "/home/c249/anaconda2/lib/python2.7/site-packages/pydot.py", line 1992, in create
stderr=subprocess.PIPE, stdout=subprocess.PIPE)
File "/home/c249/anaconda2/lib/python2.7/subprocess.py", line 390, in __init
_
errread, errwrite)
File "/home/c249/anaconda2/lib/python2.7/subprocess.py", line 1024, in _execute_child
raise child_exception
OSError: [Errno 13] Permission denied

1个回答

应该是对当前目录没有写权限吧,代码好像要产生model.png文件呢。

Bling_B
Bling_B 但是还是出现之前的问题
2 年多之前 回复
Bling_B
Bling_B 谢谢回复。我用chmod a+w给当前目录添加了写权限,
2 年多之前 回复
Csdn user default icon
上传中...
上传图片
插入图片
抄袭、复制答案,以达到刷声望分或其他目的的行为,在CSDN问答是严格禁止的,一经发现立刻封号。是时候展现真正的技术了!
其他相关推荐
ubuntu16.04安装opencv时,make不通过该怎么办?
cmake已经完成,情况如下: ``` cmake .. -- Detected version of GNU GCC: 54 (504) -- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found suitable version "1.2.8", minimum required is "1.2.3") -- Found ZLIB: /usr/lib/x86_64-linux-gnu/libz.so (found version "1.2.8") -- Checking for module 'gstreamer-base-1.0' -- No package 'gstreamer-base-1.0' found -- Checking for module 'gstreamer-video-1.0' -- No package 'gstreamer-video-1.0' found -- Checking for module 'gstreamer-app-1.0' -- No package 'gstreamer-app-1.0' found -- Checking for module 'gstreamer-riff-1.0' -- No package 'gstreamer-riff-1.0' found -- Checking for module 'gstreamer-pbutils-1.0' -- No package 'gstreamer-pbutils-1.0' found -- Checking for module 'gstreamer-base-0.10' -- No package 'gstreamer-base-0.10' found -- Checking for module 'gstreamer-video-0.10' -- No package 'gstreamer-video-0.10' found -- Checking for module 'gstreamer-app-0.10' -- No package 'gstreamer-app-0.10' found -- Checking for module 'gstreamer-riff-0.10' -- No package 'gstreamer-riff-0.10' found -- Checking for module 'gstreamer-pbutils-0.10' -- No package 'gstreamer-pbutils-0.10' found -- Looking for linux/videodev.h -- Looking for linux/videodev.h - not found -- Looking for linux/videodev2.h -- Looking for linux/videodev2.h - found -- Looking for sys/videoio.h -- Looking for sys/videoio.h - not found -- Checking for module 'libavresample' -- No package 'libavresample' found -- Looking for libavformat/avformat.h -- Looking for libavformat/avformat.h - found -- Looking for ffmpeg/avformat.h -- Looking for ffmpeg/avformat.h - not found -- Checking for module 'libgphoto2' -- No package 'libgphoto2' found -- found IPP (ICV version): 9.0.1 [9.0.1] -- at: /home/quxutao/opencv-3.1.0/3rdparty/ippicv/unpack/ippicv_lnx -- CUDA detected: 7.5 -- CUDA NVCC target flags: -gencode;arch=compute_20,code=sm_20;-gencode;arch=compute_20,code=sm_21;-gencode;arch=compute_30,code=sm_30;-gencode;arch=compute_35,code=sm_35;-gencode;arch=compute_30,code=compute_30 -- Could NOT find Doxygen (missing: DOXYGEN_EXECUTABLE) -- To enable PlantUML support, set PLANTUML_JAR environment variable or pass -DPLANTUML_JAR=<filepath> option to cmake -- Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "2.7" (found /home/quxutao/.virtualenvs/cv/bin/python) -- Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "2.6" (found /home/quxutao/.virtualenvs/cv/bin/python) -- Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "3.4" (found /home/quxutao/.virtualenvs/cv/bin/python) -- Could NOT find PythonInterp: Found unsuitable version "1.4", but required is at least "3.2" (found /home/quxutao/.virtualenvs/cv/bin/python) -- Could NOT find JNI (missing: JAVA_INCLUDE_PATH JAVA_INCLUDE_PATH2 JAVA_AWT_INCLUDE_PATH) -- Could NOT find Matlab (missing: MATLAB_MEX_SCRIPT MATLAB_INCLUDE_DIRS MATLAB_ROOT_DIR MATLAB_LIBRARIES MATLAB_LIBRARY_DIRS MATLAB_MEXEXT MATLAB_ARCH MATLAB_BIN) -- VTK is not found. Please set -DVTK_DIR in CMake to VTK build directory, or to VTK install subdirectory with VTKConfig.cmake file -- Caffe: NO -- Protobuf: YES -- Glog: NO -- HDF5: YES -- Module opencv_sfm disabled because the following dependencies are not found: Eigen Glog/Gflags -- Tesseract: NO -- HDF5: YES -- Build libprotobuf from sources: -- The protocol buffer compiler not found -- Tesseract: NO -- -- General configuration for OpenCV 3.1.0 ===================================== -- Version control: unknown -- -- Platform: -- Host: Linux 4.15.0-47-generic x86_64 -- CMake: 3.5.1 -- CMake generator: Unix Makefiles -- CMake build tool: /usr/bin/make -- Configuration: RELEASE -- -- C/C++: -- Built as dynamic libs?: YES -- C++ Compiler: /usr/bin/c++ (ver 5.4.0) -- C++ flags (Release): -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -O3 -DNDEBUG -DNDEBUG -- C++ flags (Debug): -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wundef -Winit-self -Wpointer-arith -Wshadow -Wsign-promo -Wno-narrowing -Wno-delete-non-virtual-dtor -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -fvisibility-inlines-hidden -g -O0 -DDEBUG -D_DEBUG -- C Compiler: /usr/bin/cc -- C flags (Release): -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -O3 -DNDEBUG -DNDEBUG -- C flags (Debug): -fsigned-char -W -Wall -Werror=return-type -Werror=non-virtual-dtor -Werror=address -Werror=sequence-point -Wformat -Werror=format-security -Wmissing-declarations -Wmissing-prototypes -Wstrict-prototypes -Wundef -Winit-self -Wpointer-arith -Wshadow -Wno-narrowing -fdiagnostics-show-option -Wno-long-long -pthread -fomit-frame-pointer -msse -msse2 -mno-avx -msse3 -mno-ssse3 -mno-sse4.1 -mno-sse4.2 -ffunction-sections -fvisibility=hidden -g -O0 -DDEBUG -D_DEBUG -- Linker flags (Release): -- Linker flags (Debug): -- Precompiled headers: YES -- Extra dependencies: /usr/lib/x86_64-linux-gnu/libpng.so /usr/lib/x86_64-linux-gnu/libtiff.so /usr/lib/x86_64-linux-gnu/libjasper.so /usr/lib/x86_64-linux-gnu/libjpeg.so gtk-3 gdk-3 pangocairo-1.0 pango-1.0 atk-1.0 cairo-gobject cairo gdk_pixbuf-2.0 gio-2.0 gobject-2.0 gthread-2.0 glib-2.0 dc1394 v4l1 v4l2 avcodec-ffmpeg avformat-ffmpeg avutil-ffmpeg swscale-ffmpeg /usr/lib/x86_64-linux-gnu/libbz2.so /usr/lib/x86_64-linux-gnu/hdf5/openmpi/lib/libhdf5.so /usr/lib/x86_64-linux-gnu/libsz.so /usr/lib/x86_64-linux-gnu/libz.so /usr/lib/x86_64-linux-gnu/libdl.so /usr/lib/x86_64-linux-gnu/libm.so dl m pthread rt cudart nppc nppi npps cufft -L/usr/lib/x86_64-linux-gnu -- 3rdparty dependencies: libwebp IlmImf libprotobuf -- -- OpenCV modules: -- To be built: cudev core cudaarithm flann hdf imgproc ml reg surface_matching video cudabgsegm cudafilters cudaimgproc cudawarping dnn fuzzy imgcodecs photo shape videoio cudacodec highgui objdetect plot ts xobjdetect xphoto bgsegm bioinspired dpm face features2d line_descriptor saliency text calib3d ccalib cudafeatures2d cudalegacy cudaobjdetect cudaoptflow cudastereo datasets rgbd stereo structured_light superres tracking videostab xfeatures2d ximgproc aruco optflow stitching -- Disabled: world contrib_world -- Disabled by dependency: - -- Unavailable: java python2 python3 viz cvv matlab sfm -- -- GUI: -- QT: NO -- GTK+ 3.x: YES (ver 3.18.9) -- GThread : YES (ver 2.48.2) -- GtkGlExt: NO -- OpenGL support: NO -- VTK support: NO -- -- Media I/O: -- ZLib: /usr/lib/x86_64-linux-gnu/libz.so (ver 1.2.8) -- JPEG: /usr/lib/x86_64-linux-gnu/libjpeg.so (ver ) -- WEBP: build (ver 0.3.1) -- PNG: /usr/lib/x86_64-linux-gnu/libpng.so (ver 1.2.54) -- TIFF: /usr/lib/x86_64-linux-gnu/libtiff.so (ver 42 - 4.0.6) -- JPEG 2000: /usr/lib/x86_64-linux-gnu/libjasper.so (ver 1.900.1) -- OpenEXR: build (ver 1.7.1) -- GDAL: NO -- -- Video I/O: -- DC1394 1.x: NO -- DC1394 2.x: YES (ver 2.2.4) -- FFMPEG: YES -- codec: YES (ver 56.60.100) -- format: YES (ver 56.40.101) -- util: YES (ver 54.31.100) -- swscale: YES (ver 3.1.101) -- resample: NO -- gentoo-style: YES -- GStreamer: NO -- OpenNI: NO -- OpenNI PrimeSensor Modules: NO -- OpenNI2: NO -- PvAPI: NO -- GigEVisionSDK: NO -- UniCap: NO -- UniCap ucil: NO -- V4L/V4L2: Using libv4l1 (ver 1.10.0) / libv4l2 (ver 1.10.0) -- XIMEA: NO -- Xine: NO -- gPhoto2: NO -- -- Parallel framework: pthreads -- -- Other third-party libraries: -- Use IPP: 9.0.1 [9.0.1] -- at: /home/quxutao/opencv-3.1.0/3rdparty/ippicv/unpack/ippicv_lnx -- Use IPP Async: NO -- Use VA: NO -- Use Intel VA-API/OpenCL: NO -- Use Eigen: NO -- Use Cuda: YES (ver 7.5) -- Use OpenCL: YES -- Use custom HAL: NO -- -- NVIDIA CUDA -- Use CUFFT: YES -- Use CUBLAS: NO -- USE NVCUVID: NO -- NVIDIA GPU arch: 20 21 30 35 -- NVIDIA PTX archs: 30 -- Use fast math: NO -- -- OpenCL: -- Version: dynamic -- Include path: /home/quxutao/opencv-3.1.0/3rdparty/include/opencl/1.2 -- Use AMDFFT: NO -- Use AMDBLAS: NO -- -- Python 2: -- Interpreter: NO -- -- Python 3: -- Interpreter: NO -- -- Python (for build): NO -- -- Java: -- ant: NO -- JNI: NO -- Java wrappers: NO -- Java tests: NO -- -- Matlab: Matlab not found or implicitly disabled -- -- Documentation: -- Doxygen: NO -- PlantUML: NO -- -- Tests and samples: -- Tests: YES -- Performance tests: YES -- C/C++ Examples: YES -- -- Install path: /usr/local -- -- cvconfig.h is in: /home/quxutao/opencv-3.1.0/build -- ----------------------------------------------------------------- -- -- Configuring done -- Generating done -- Build files have been written to: /home/quxutao/opencv-3.1.0/build ``` 但是make的时候,就报错: ``` make [ 4%] Built target libwebp [ 4%] Built target IlmImf [ 4%] Built target opencv_cudev [ 4%] Built target opencv_core_pch_dephelp [ 4%] Built target pch_Generate_opencv_core [ 4%] Building NVCC (Device) object modules/core/CMakeFiles/cuda_compile.dir/src/cuda/cuda_compile_generated_gpu_mat.cu.o /usr/include/string.h: In function ‘void* __mempcpy_inline(void*, const void*, size_t)’: /usr/include/string.h:652:42: error: ‘memcpy’ was not declared in this scope return (char *) memcpy (__dest, __src, __n) + __n; ^ CMake Error at cuda_compile_generated_gpu_mat.cu.o.cmake:266 (message): Error generating file /home/quxutao/opencv-3.1.0/build/modules/core/CMakeFiles/cuda_compile.dir/src/cuda/./cuda_compile_generated_gpu_mat.cu.o modules/core/CMakeFiles/opencv_core.dir/build.make:399: recipe for target 'modules/core/CMakeFiles/cuda_compile.dir/src/cuda/cuda_compile_generated_gpu_mat.cu.o' failed make[2]: *** [modules/core/CMakeFiles/cuda_compile.dir/src/cuda/cuda_compile_generated_gpu_mat.cu.o] Error 1 CMakeFiles/Makefile2:2307: recipe for target 'modules/core/CMakeFiles/opencv_core.dir/all' failed make[1]: *** [modules/core/CMakeFiles/opencv_core.dir/all] Error 2 Makefile:160: recipe for target 'all' failed make: *** [all] Error 2 ``` 弄了一下午了,没有找到相关的解决办法。我的cuda是7.5,其实不用GPU也可以的,我只是想用KAZE滤波。。。跪求大神帮忙。。。
用vscope编写python程序,运行后无结果显示?
import numpy as np import matplotlib.pyplot as plt t = np.arange(0, 4, 0.1) plt.plot(t,t,t,t+2,t,t**2) ``` numpy包和matplotlib都安装到最新版本。 ```
matplotlib如何做分组条形图??
## matplotlib如何做分组条形图?? ![图片说明](https://img-ask.csdn.net/upload/202002/03/1580722482_16424.png) 这是需要可视化的数据,**想绘制成分组条形图**,下图是通过seaborn实现的效果, ![图片说明](https://img-ask.csdn.net/upload/202002/03/1580722541_765354.png) 有没有大神指导下如何用matplotlib实现??或者使用pandas.plot实现???
r语言 legend 不显示也不报错如何修改让它显示出来
``` # Set up our time increment and our vector (array) of x (time) values deltaX = 1 # years maxX = 100 # years x = seq(0,maxX,deltaX) # years # Annual growth rate of the investment r = 0.15 c = 0.08 # A vector to hold the value of the investment P = vector(length=length(x)) # Initial value of the investment P[1] = 400 P_Max = 5000 # no fishing for (i in 2:length(x)) { P[i] = P[i-deltaX] + r*deltaX*(1-P[i-deltaX]/P_Max)*P[i-deltaX] } plot(x = x, y = P, type = "l", xlab="Time (years)",ylab="number of fish",col="green") # before for (i in 2:length(x)) { P_e = P[i-deltaX]*(1-c) P[i] = P_e + r*deltaX*(1-P_e/P_Max)*P_e } lines(x = x, y = P, type = "l",col="red") # after for (i in 2:length(x)) { P[i] = P[i-deltaX] + r*deltaX*(1-P[i-deltaX]/P_Max)*P[i-deltaX] P[i] = P[i]*(1-c) } lines(x = x, y = P, type = "l", col="blue") legend(x=1,y=95,legend = c("No fishing","Before fishing","After fishing") ,col=c("green","red","blue"),lty=1) ``` 不知道为什么 legend 显示不出来请帮助解决一下
报错Traceback (most recent call last): File... .format(val=len(data), ind=len(index))) ValueError: Length of passed values is 400, index implies 1
我是个小菜鸟,在尝试写生成高斯分布的作业时被报错: ``` D:\Anaconda\python.exe "F:/All tasks in BFU/Study abroad/Internship2019.8 in Google/Homework/Course1/Exercise6/exercise6.py" Traceback (most recent call last): File "F:/All tasks in BFU/Study abroad/Internship2019.8 in Google/Homework/Course1/Exercise6/exercise6.py", line 20, in <module> y = func(x, mean, std) File "F:/All tasks in BFU/Study abroad/Internship2019.8 in Google/Homework/Course1/Exercise6/exercise6.py", line 15, in func f = math.exp(-((x - mu) ^ 2)/(2*sigma ^ 2))/(sigma * math.sqrt(2 * math.pi)) File "D:\Anaconda\lib\site-packages\pandas\core\ops.py", line 1071, in wrapper index=left.index, name=res_name, dtype=None) File "D:\Anaconda\lib\site-packages\pandas\core\ops.py", line 980, in _construct_result out = left._constructor(result, index=index, dtype=dtype) File "D:\Anaconda\lib\site-packages\pandas\core\series.py", line 262, in __init__ .format(val=len(data), ind=len(index))) ValueError: Length of passed values is 400, index implies 1 Process finished with exit code 1 ``` 我有安装anaconda,但是报错中貌似表明panda这个package的问题。请问大神大佬,我存在什么问题呀应该怎么解决⊙︿⊙,我好像没在网上找到和我一样的问题,不敢和网上的回答一样在命令提示符里输入命令怕搞错(。•́︿•̀。),是我比较菜鸟又急着所以麻烦了!! 附上我的作业代码: ``` import math import pandas as pd import numpy as np import matplotlib.pyplot as plt # import matplotlib.mlab as mlb data = pd.read_csv('example-exercise6.csv') # read file of data # data = data_['time'] mean = data.mean() # average of data std = data.std() # std def func(x, mu, sigma): f = math.exp(-((x - mu) ^ 2)/(2*sigma ^ 2))/(sigma * math.sqrt(2 * math.pi)) return f x = np.arange(60, 100, 0.1) y = func(x, mean, std) plt.plot(x, y) plt.hist(data, bins=10, rwidth=0.9, normed=True) # x = np.arange(145, 155,0.2) # y = normfun(x, mean, std) # plt.plot(x,y,'g',linewidth = 3) # plt.hist(data, bins = 6, color = 'b', alpha=0.5, rwidth = 0.9, normed=True) # plt.title('stakes distribution') # plt.xlabel('stakes time') # plt.ylabel('Probability') plt.show() ``` ( 其中csv文件是:) ``` 87 88 83 83 86 80 84 90 84 80 94 89 76 ```
R中使用plot函数~是什么意思
plot(a,b) 和 plot(b~a)有什么去别呢,用两组数据点实验得到结果一样的。 但绘制箱型图时候多用~符号
是否有其他技巧能够获取 fastai learn类的损失和学习率的关系图
**我使用fastai的learn类在远程云服务器上进行训练,** **通常我们会根据学习率与损失的关系进行学习率的调整,** ``` learn.lr_find() learn.recorder.plot() ``` **但是由于某种原因,远程服务器无法返回这个关系图** **我们是否有其他方法能够保存他们之间的关系,在本地进行绘制?**
mxnet中'DataLoader' object is not callable是什么情况,我是按书这么写的
这是一个高位线性回归实验的代码,具体在动手学深度学习mxnet版的p66-p68页 %matplotlib inline import d2lzh as d2l from mxnet import autograd, gluon, init, nd from mxnet.gluon import data as gdata, loss as gloss, nn n_train, n_test, num_inputs = 20, 100, 200 true_w, true_b = nd.ones((num_inputs, 1)) * 0.01, 0.05 features = nd.random.normal(shape=(n_train + n_test, num_inputs)) labels = nd.dot(features, true_w) + true_b labels += nd.random.normal(scale=0.01, shape=labels.shape) train_features, test_features = features[:n_train, :], features[n_train:, :] train_labels, test_labels = labels[:n_train], labels[n_train:] def init_params(): w = nd.random.normal(scale=1, shape=(num_inputs, 1)) b = nd.zeros(shape=(1,)) w.attach_grad() b.attach_grad() return [w,b] def l2_penalty(w): return (w**2).sun() / 2 batch_size, num_epochs, lr = 1, 100, 0.003 net, loss = d2l.linreg, d2l.squared_loss train_iter = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True, num_workers=0) def fit_and_plot(lambd): w, b = init_params() train_ls, test_ls = [], [] for _ in range(num_epochs): for X, y in train_iter(): with autograd.record(): #添加了L2范数惩罚项 l = loss(net(X, w, b), y) + lambd * l2_penalty(w) l.backward() d2l.sgd([w, b], lr, batch_size) train_ls.append(loss(net(train_features, w, b), train_labels).mean().asscalar()) test_ls.append(loss(net(test_features, w, b), test_labels).mean().asscalar()) d2l.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'loss', range(1, num_epochs + 1), test_ls, ['train', ' test']) print('L2 norm of w:', w.norm().asscalar()) fit_and_plot(lambd=0) 这一些代码编译之后 出现是这个 --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-25-d042fb36ac1a> in <module> ----> 1 fit_and_plot(lambd=0) <ipython-input-24-1a36f25d56d9> in fit_and_plot(lambd) 7 train_ls, test_ls = [], [] 8 for _ in range(num_epochs): ----> 9 for X, y in train_iter(): 10 with autograd.record(): 11 #添加了L2范数惩罚项 TypeError: 'DataLoader' object is not callable 想问问 各位大佬,谢谢了
matplotlib库在ubuntu下的一个bug?
当我在Ubuntu 16.10下使用matplotlib绘制以下程序的图形时: 程序是: ``` # -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 10, 1000) y = np.sin(x) z = np.cos(x**2) plt.figure(figsize=(8,4)) plt.plot(x,y,label="$sin(x)$",color="red",linewidth=2) plt.plot(x,z,"b--",label="$cos(x^2)$") plt.xlabel("Time(s)") plt.ylabel("Volt") plt.title("PyPlot First Example") plt.ylim(-1.2,1.2) plt.legend() plt.show() ``` 这是官方给出的win下的标准输出: ![图片说明](https://img-ask.csdn.net/upload/201704/15/1492257795_541926.png) 然而,我在Ubuntu 16.10下得到的输出却是: ![图片说明](https://img-ask.csdn.net/upload/201704/15/1492257835_241238.png) 右上角蓝色虚线后应该是上图一样显示cosx的平方的,但实际得到的却不一样,这是为何?
在使用anconda的matploylib时出现"namespace这样的错误,请问是什么原因?该如何解决?
在我的电脑上运行一下代码时报错: 代码 ``` import matplotlib.pyplot as plt squres=[1,4,9,16,25] plt.plot(squres) plt.show( ) ``` 这是报错: ![图片说明](https://img-ask.csdn.net/upload/202001/03/1578042212_463527.png) 在别人的电脑上运行(同样的代码)结果: ![图片说明](https://img-ask.csdn.net/upload/202001/03/1578042322_446180.jpg) 请问该怎么解决?什么原因?报错是什么意思呢?
Message Decowding 的问题
Problem Description The cows are thrilled because they've just learned about encrypting messages. They think they will be able to use secret messages to plot meetings with cows on other farms. Cows are not known for their intelligence. Their encryption method is nothing like DES or BlowFish or any of those really good secret coding methods. No, they are using a simple substitution cipher. The cows have a decryption key and a secret message. Help them decode it. The key looks like this: yrwhsoujgcxqbativndfezmlpk Which means that an 'a' in the secret message really means 'y'; a 'b' in the secret message really means 'r'; a 'c' decrypts to 'w'; and so on. Blanks are not encrypted; they are simply kept in place. Input text is in upper or lower case, both decrypt using the same decryption key, keeping the appropriate case, of course. Input * Line 1: 26 lower case characters representing the decryption key * Line 2: As many as 80 characters that are the message to be decoded Output * Line 1: A single line that is the decoded message. It should have the same length as the second line of input. Sample Input eydbkmiqugjxlvtzpnwohracsf Kifq oua zarxa suar bti yaagrj fa xtfgrj Sample Output Jump the fence when you seeing me coming
plot画图出现Vectors must be the same lengths. 不知道哪里的问题
``` x=1:.1;2; y=3*(0.0227+0.77*cosd(15*x-183.62))/sqrt(1-(0.0227+0.77*cosd(15*x-183.62)).^2); plot(x,y) ```
python 画图plot画图时数据太多,图看起来不好看。请问怎么拉长X轴,或者增加每个点之间的间隔
数据太多了,想把x轴拉长,或者有没有那种可以用鼠标拖到的作图方式,方便看![图片说明](https://img-ask.csdn.net/upload/202001/03/1578047049_268091.png)求大佬!!!!
用MATLAB实现BP神经网络对二手车价格的预测误差很大
基于BP神经网络建立了二手车价格预测模型,建立的双隐层结构。 思路为根据二手的品牌、里程、车龄等信息预测二手车价格。共3500条数据,3000训练,500调试。 但是不管怎么调试一直误差很大。求大神帮忙调试一下,怎么降低误差。 ``` clear; clc; % 清空环境变量 % 原始数据 %名字 name = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','A1:A3000'); %新车指导价 new_price = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','B1:B3000'); %表现里程 mileage = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','C1:C3000'); %排放标准 discharge = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','D1:D3000'); %变速箱类型 transmission_case = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','E1:E3000'); %排量 displacement = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','F1:F3000'); %过户次数 transfer = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','G1:G3000'); %车龄 age = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','H1:H3000'); %二手车价格 price = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','I1:I3000'); %目标数据 ptest = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','A3001:H3475'); ptest = ptest'; ttest = xlsread('E:\毕业论文\Data\瓜子二手车数据处理.xlsx','I3001:I3475'); ttest = ttest'; % 输入数据矩阵 p = [name';new_price';mileage';discharge';transmission_case';displacement';transfer';age']; %输入目标矩阵 t = [price']; %数据归一化 [pn, minp, maxp, tn, mint, maxt] = premnmx(p, t); %隐含层节点数 NodeNum1 = 20; NodeNum2 = 20; %输出维数 TypeNum = 1; TF1 = 'tansig'; TF2 = 'tansig'; TF3 = 'tansig'; net = newff(minmax(pn),[NodeNum1,NodeNum2,TypeNum],{TF1 TF2 TF3},'traingdx'); %网络创建traingdm %每间隔50步显示一次训练 net.trainParam.show = 50; %最大训练次数 net.trainParam.epochs = 50000; %训练所要达到的精度 net.trainParam.goal = 1e-4; %学习速率 net.trainParam.lr = 0.0001; %最小梯度 net.trainParam.min_grad = 1e-15; net = train(net,pn,tn); %测试数据的归一化 p2n = tramnmx(ptest,minp,maxp); an = sim(net,p2n); %数据的反归一化,即最终想得到的预测结果 [a] = postmnmx(an,mint,maxt); plot(1:length(ttest),ttest,'o',1:length(ttest),a,'+'); title('o表示预测值--- *表示实际值'); grid on m = length(a); t1 = [ttest]; error = (t1-a)./a; figure plot(1:length(error),error,'o'); title('误差变化图'); grid on ``` 结果图片是: ![图片说明](https://img-ask.csdn.net/upload/202001/14/1578988212_623706.jpg) ![图片说明](https://img-ask.csdn.net/upload/202001/14/1578988228_219711.jpg) 数据截图![图片说明](https://img-ask.csdn.net/upload/202001/14/1578988287_246300.png)
matplotlib绘图时报错
**目前需要解决的问题:** 第一种情况测试,如果subplot2和subplot1不共享x轴,candlestick2_ohlc加上(代码块中被我注释掉了),就不会报错,第二种情况测试,如果共享x轴,去掉candlestick2_ohlc也不报错,但是不去掉sharex=subplot1,第三种情况,就是目前的我需要解决的问题,同时运行candlestick2_ohlc,,并且sharex=subplot1函就会报错,以下是全错信息以及全部代码。 **请运行时把#candlestick2_ohlc(ax=subplot1,opens=historty['open'],highs=historty['high'],lows=historty['low'],closes=historty['close'],width=0.5,colordown='g',colorup='red')的注释取消以查看错误信息。** **报错信息:** ``` view limit minimum -36854.55 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units ``` **主要代码:** ``` import pandas import matplotlib import matplotlib.pyplot as plt from mpl_finance import candlestick2_ohlc def stockPricePlot(ticker): #Step1. Load Data historty = pandas.read_csv('./Data/TickerList_CN/daily_history/'+ticker+'.csv',parse_dates=True,index_col=0) #Step2. Data Maniulation volume = historty['volume'] volume = volume.reset_index() volume['timestamp'] = volume['timestamp'].map(matplotlib.dates.date2num) # print('volume-timestamp',volume['timestamp']) ohlc = historty[['open','high','low','close']] ohlc = ohlc.reset_index() #把原索引列timestamp列变成非索引列,索引列重置为012346... ohlc['timestamp'] = ohlc['timestamp'].map(matplotlib.dates.date2num) print(ohlc) #Step3. Plot Figures .Subplot1: scatter plot .Subplot2.candle stick plot. subplot1 = plt.subplot2grid((2,1),(0,0),rowspan=1,colspan=1) subplot1.plot(volume['timestamp'],volume) subplot1.xaxis_date() subplot2 = plt.subplot2grid((2,1),(1,0),rowspan=1,colspan=1,sharex=subplot1) #candlestick2_ohlc(ax=subplot1,opens=historty['open'],highs=historty['high'],lows=historty['low'],closes=historty['close'],width=0.5,colordown='g',colorup='red') plt.show() stockPricePlot('000830') ``` **部分csv内容:** ``` timestamp,open,high,close,low,volume,price_change,p_change,ma5,ma10,ma20,v_ma5,v_ma10,v_ma20 2018-01-02,15.99,17.34,16.9,15.98,757629.44,0.98,6.16,15.86,15.956,16.004,548371.81,530310.18,623883.69 2018-01-03,17.13,17.2,17.15,16.6,638103.5,0.25,1.48,16.136,16.101,16.078,551871.92,533343.05,608798.69 2018-01-04,17.17,18.48,18.04,16.91,887199.19,0.89,5.19,16.644,16.35,16.174,641565.9,575043.65,612220.87 2018-01-05,17.85,18.28,17.98,17.81,468611.03,-0.06,-0.33,17.198,16.523,16.281,646149.99,565940.01,593073.06 2018-01-08,17.99,18.86,18.68,17.81,682480.5,0.7,3.89,17.75,16.765,16.431,686804.73,587491.86,589820.04 2018-01-09,18.5,18.58,18.37,18.01,461303.56,-0.31,-1.66,18.044,16.952,16.542,627539.56,587955.68,581795.08 2018-01-10,18.26,18.27,17.98,17.81,394598.0,-0.39,-2.12,18.21,17.173,16.624,578838.46,565355.19,569994.99 2018-01-11,17.91,18.88,18.51,17.7,522358.72,0.53,2.95,18.304,17.474,16.701,505870.36,573718.13,565724.87 2018-01-12,18.45,18.7,18.13,17.9,403101.12,-0.38,-2.05,18.334,17.766,16.764,492768.38,569459.19,554347.3 2018-01-15,18.0,18.48,17.76,17.6,440299.28,-0.37,-2.04,18.15,17.95,16.85,444332.14,565568.43,542099.03 2018-01-16,17.79,18.64,18.58,17.79,541583.88,0.82,4.62,18.192,18.118,17.037,460388.2,543963.88,537137.03 2018-01-17,18.58,18.66,18.34,17.91,453619.47,-0.24,-1.29,18.264,18.237,17.169,472192.49,525515.48,529429.26 2018-01-18,18.4,19.46,19.46,18.4,674142.25,1.12,6.11,18.454,18.379,17.365,502549.2,504209.78,539626.72 2018-01-19,19.23,19.44,19.28,19.06,394495.88,-0.18,-0.93,18.684,18.509,17.516,500828.15,496798.27,531369.14 2018-01-22,19.44,20.34,20.16,19.42,500034.16,0.88,4.56,19.164,18.657,17.711,512775.13,478553.63,533022.74 2018-01-23,20.06,20.66,20.2,19.97,475477.88,0.04,0.2,19.488,18.84,17.896,499553.93,479971.06,533963.37 2018-01-24,20.06,21.14,20.78,20.05,474656.88,0.58,2.87,19.976,19.12,18.147,503761.41,487976.95,526666.07 2018-01-25,20.78,21.58,21.1,20.31,610635.38,0.32,1.54,20.304,19.379,18.427,491060.04,496804.62,535261.37 2018-01-26,21.17,21.23,20.99,20.17,507878.75,-0.11,-0.52,20.646,19.665,18.716,513736.61,507282.38,538370.78 2018-01-29,21.7,22.35,21.32,21.23,653364.38,0.33,1.57,20.878,20.021,18.986,544402.65,528588.89,547078.66 2018-01-30,21.59,23.38,23.28,21.57,742671.62,1.96,9.19,21.494,20.491,19.305,597841.4,548697.67,546330.77 2018-01-31,23.0,24.38,22.59,22.05,942972.56,-0.69,-2.96,21.856,20.916,19.577,691504.54,597632.97,561574.22 2018-02-01,22.88,23.49,22.88,22.34,612182.88,0.29,1.28,22.212,21.258,19.819,691814.04,591437.04,547823.41 2018-02-02,22.5,23.99,23.89,21.53,650898.12,1.01,4.41,22.792,21.719,20.114,720417.91,617077.26,556937.76 2018-02-05,23.29,24.96,24.9,23.16,648181.88,1.01,4.23,23.508,22.193,20.425,719381.41,631892.03,555222.83 2018-02-06,24.4,25.1,23.44,23.22,777855.06,-1.46,-5.86,23.54,22.517,20.679,726418.1,662129.75,571050.41 2018-02-07,24.2,24.88,21.1,21.1,1272183.25,-2.34,-9.98,23.242,22.549,20.835,792260.24,741882.39,614929.67 2018-02-08,20.62,21.11,21.0,19.78,1028494.56,-0.1,-0.47,22.866,22.539,20.959,875522.57,783668.31,640236.46 2018-02-09,19.9,20.08,18.9,18.9,608822.0,-2.1,-10.0,21.868,22.33,20.998,867107.35,793762.63,650522.51 2018-02-12,18.5,20.0,19.5,17.98,812573.19,0.6,3.17,20.788,22.148,21.085,899985.61,809683.51,669136.2 2018-02-13,19.93,21.18,20.86,19.65,772670.31,1.36,6.97,20.272,21.906,21.199,898948.66,812683.38,680690.52 2018-02-14,20.86,21.65,21.28,20.83,577215.25,0.42,2.01,20.308,21.775,21.346,759955.06,776107.65,686870.31 2018-02-22,21.8,23.35,23.24,21.63,712053.19,1.96,9.21,20.756,21.811,21.535,696666.79,786094.68,688765.86 2018-02-23,23.32,23.94,23.46,22.39,825140.31,0.22,0.95,21.668,21.768,21.744,739930.45,803518.9,710298.08 2018-02-26,23.64,24.51,24.45,23.02,719122.62,0.99,4.22,22.658,21.723,21.958,721240.34,810612.97,721252.5 2018-02-27,24.19,24.4,23.18,23.15,757924.06,-1.27,-5.19,23.122,21.697,22.107,718291.09,808619.87,735374.81 2018-02-28,22.64,23.58,22.98,22.25,612434.44,-0.2,-0.86,23.462,21.885,22.217,725334.92,742644.99,742263.69 2018-03-01,22.54,23.43,22.5,22.41,600968.94,-0.48,-2.09,23.314,22.035,22.287,703118.07,699892.43,741780.37 2018-03-02,22.49,23.03,22.59,21.88,681435.12,0.09,0.4,23.14,22.404,22.367,674377.04,707153.74,750458.19 2018-03-05,22.64,22.75,20.33,20.33,1008017.69,-2.26,-10.0,22.316,22.487,22.318,732156.05,726698.19,768190.85 2018-03-06,20.03,21.15,21.1,19.46,1046506.31,0.77,3.79,21.9,22.511,22.209,789872.5,754081.79,783382.59 2018-03-07,20.98,20.98,19.35,19.0,1157634.38,-1.75,-8.29,21.174,22.318,22.047,898912.49,812123.71,794115.68 2018-03-08,19.24,19.86,19.71,19.1,746500.88,0.36,1.86,20.616,21.965,21.888,928018.88,815568.48,800831.58 2018-03-09,19.78,19.79,19.4,19.31,597694.69,-0.31,-1.57,19.978,21.559,21.664,911270.79,792823.91,798171.41 2018-03-12,19.51,20.32,20.22,18.91,1255197.12,0.82,4.23,19.956,21.136,21.43,960706.68,846431.36,828522.17 2018-03-13,20.08,20.87,20.58,19.89,1038786.5,0.36,1.78,19.852,20.876,21.287,959162.71,874517.61,841568.74 2018-03-14,20.39,20.74,20.33,20.01,639427.25,-0.25,-1.22,20.048,20.611,21.248,855521.29,877216.89,809930.94 2018-03-15,20.25,20.37,19.8,19.45,655907.06,-0.53,-2.61,20.066,20.341,21.188,837402.52,882710.7,791301.57 2018-03-16,19.92,20.35,19.77,19.74,534175.62,-0.03,-0.15,20.14,20.059,21.232,824698.71,867984.75,787569.25 2018-03-19,19.45,19.46,18.14,17.79,1195104.25,-1.63,-8.24,19.724,19.84,21.164,812680.14,886693.41,806695.8 2018-03-20,17.7,18.09,17.86,17.43,604412.62,-0.28,-1.54,19.18,19.516,21.014,725805.36,842484.04,798282.92 2018-03-21,17.85,18.12,17.11,16.9,849386.25,-0.75,-4.2,18.536,19.292,20.805,767797.16,811659.22,811891.47 2018-03-22,17.12,17.6,17.3,17.02,593567.62,0.19,1.11,18.036,19.051,20.508,755329.27,796365.9,805967.19 2018-03-23,16.6,16.74,15.67,15.6,906732.19,-1.63,-9.42,17.216,18.678,20.119,829840.59,827269.65,810046.78 2018-03-26,15.7,16.42,16.35,15.45,699463.69,0.68,4.34,16.858,18.291,19.714,730712.47,771696.31,809063.83 2018-03-27,16.88,17.29,17.13,16.68,745425.44,0.78,4.77,16.712,17.946,19.411,758915.04,742360.2,808438.9 2018-03-28,16.81,17.0,16.18,15.81,844269.5,-0.95,-5.55,16.526,17.531,19.071,757891.69,762844.42,820030.66 2018-03-29,16.3,17.79,17.52,16.03,1062661.88,1.34,8.28,16.57,17.303,18.822,851710.54,803519.91,843115.3 2018-03-30,17.49,17.92,17.56,16.95,821160.5,0.04,0.23,16.948,17.082,18.571,834596.2,832218.39,850101.57 2018-04-02,17.43,18.37,17.79,17.33,749252.0,0.23,1.31,17.236,17.047,18.444,844553.86,787633.17,837163.29 2018-04-03,17.3,17.45,17.16,16.81,610011.38,-0.63,-3.54,17.242,16.977,18.247,817471.05,788193.05,815338.54 2018-04-04,17.31,17.57,16.88,16.85,467604.41,-0.28,-1.63,17.382,16.954,18.123,742138.03,750014.86,780837.04 2018-04-09,17.1,17.21,16.47,15.92,501334.31,-0.41,-2.43,17.172,16.871,17.961,629872.52,740791.53,768578.71 2018-04-10,16.65,17.56,17.54,16.4,716081.75,1.07,6.5,17.168,17.058,17.868,608856.77,721726.49,774498.07 2018-04-11,17.9,18.25,17.78,17.45,866904.25,0.24,1.37,17.166,17.201,17.746,632387.22,738470.54,755083.42 2018-04-12,18.25,18.25,17.78,17.5,668276.25,0.0,0.0,17.29,17.266,17.606,644040.19,730755.62,736557.91 2018-04-13,18.0,18.12,17.88,17.55,556391.44,0.1,0.56,17.49,17.436,17.484,661797.6,701967.82,732406.12 2018-04-16,17.81,17.9,17.44,17.03,617045.06,-0.44,-2.46,17.684,17.428,17.366,684939.75,657406.14,730463.02 2018-04-17,17.3,17.3,16.67,16.54,625413.12,-0.77,-4.42,17.51,17.339,17.211,666806.02,637831.4,735024.9 2018-04-18,16.85,17.28,17.22,16.25,638213.0,0.55,3.3,17.398,17.282,17.165,621067.77,626727.5,707180.33 2018-04-19,17.26,17.85,17.52,17.11,639811.69,0.3,1.74,17.346,17.318,17.148,615374.86,629707.53,708950.29 2018-04-20,17.33,17.33,16.62,16.57,650977.19,-0.9,-5.14,17.094,17.292,17.123,634292.01,648044.81,699029.83 2018-04-23,16.62,17.05,16.93,16.4,382524.53,0.31,1.86,16.992,17.338,17.105,587387.91,636163.83,688477.68 2018-04-24,16.8,18.17,18.02,16.77,1069987.38,1.09,6.44,17.262,17.386,17.222,676302.76,671554.39,696640.44 2018-04-25,17.8,17.95,17.89,17.65,611291.88,-0.13,-0.72,17.396,17.397,17.299,670918.53,645993.15,692231.85 2018-04-26,17.92,17.99,17.08,17.03,760669.38,-0.81,-4.53,17.308,17.327,17.297,695090.07,655232.47,692994.05 2018-04-27,17.19,17.59,17.29,16.83,511055.41,0.21,1.23,17.442,17.268,17.352,667105.72,650698.86,676333.34 2018-05-02,17.33,17.58,17.58,17.0,504534.41,0.29,1.68,17.572,17.282,17.355,691507.69,639447.8,648426.97 2018-05-03,17.39,18.12,17.89,17.08,813721.5,0.31,1.76,17.546,17.404,17.372,640254.52,658278.64,648055.02 2018-05-04,17.84,18.5,18.18,17.64,938110.5,0.29,1.62,17.604,17.5,17.391,705618.24,688268.39,657497.94 2018-05-07,18.26,19.59,19.27,18.23,1107393.38,1.09,6.0,18.042,17.675,17.497,774963.04,735026.56,682367.04 2018-05-08,19.35,19.81,19.57,19.11,859601.12,0.3,1.56,18.498,17.97,17.631,844672.18,755888.95,701966.88 2018-05-09,19.57,19.86,19.74,19.37,657269.62,0.17,0.87,18.93,18.251,17.795,875219.22,783363.46,709763.64 2018-05-10,19.87,20.28,19.6,19.31,896830.94,-0.14,-0.71,19.272,18.409,17.898,891841.11,766047.81,718801.1 2018-05-11,19.6,19.69,18.95,18.84,859166.38,-0.65,-3.32,19.426,18.515,17.956,876052.29,790835.26,718414.21 2018-05-14,18.95,19.31,18.87,18.72,564472.19,-0.08,-0.42,19.346,18.694,18.011,767468.05,771215.55,713224.01 2018-05-15,18.8,19.73,19.67,18.39,802182.19,0.8,4.24,19.366,18.932,18.1,755984.26,800328.22,725513.54 2018-05-16,19.44,19.58,19.15,19.05,623762.62,-0.52,-2.64,19.248,19.089,18.186,749282.86,812251.04,725849.42 2018-05-17,19.15,20.27,19.75,19.15,940693.81,0.6,3.13,19.278,19.275,18.34,758055.44,824948.28,741613.46 2018-05-18,19.93,20.86,20.8,19.93,1222465.62,1.05,5.32,19.648,19.537,18.519,830715.29,853383.79,770826.09 2018-05-21,21.25,21.94,21.26,20.66,1324208.0,0.46,2.21,20.126,19.736,18.706,982662.45,875065.25,805045.9 2018-05-22,21.41,21.88,21.49,21.1,841641.94,0.23,1.08,20.49,19.928,18.949,990554.4,873269.33,814579.14 2018-05-23,21.65,21.8,20.88,20.68,839241.12,-0.61,-2.84,20.836,20.042,19.147,1033650.1,891466.48,837414.97 2018-05-24,20.85,21.35,21.05,20.46,582306.88,0.17,0.81,21.096,20.187,19.298,961972.71,860014.08,813030.94 2018-05-25,20.8,21.0,20.41,20.3,485541.75,-0.64,-3.04,21.018,20.333,19.424,814587.94,822651.61,806743.44 2018-05-28,20.05,20.73,20.38,19.81,472109.69,-0.03,-0.15,20.842,20.484,19.589,644168.28,813415.36,792315.45 2018-05-29,20.3,20.46,19.91,19.72,478850.41,-0.47,-2.31,20.526,20.508,19.72,571609.97,781082.18,790705.2 2018-05-30,19.4,19.4,18.7,18.7,568306.75,-1.21,-6.08,20.09,20.463,19.776,517423.1,775536.6,793893.82 2018-05-31,19.01,19.52,19.45,18.78,521848.62,0.75,4.01,19.77,20.433,19.854,505331.44,733652.08,779300.18 2018-06-01,19.21,19.28,18.77,18.55,476589.69,-0.68,-3.5,19.442,20.23,19.884,503541.03,659064.49,756224.14 2018-06-04,19.03,19.39,19.12,19.0,421888.41,0.35,1.86,19.19,20.016,19.876,493496.78,568832.53,721948.89 2018-06-05,19.29,19.77,19.76,19.07,449022.41,0.64,3.35,19.16,19.843,19.886,487531.18,529570.57,701419.95 2018-06-06,19.77,19.77,19.57,19.37,379771.12,-0.19,-0.96,19.334,19.712,19.877,449824.05,483623.57,687545.03 2018-06-07,19.66,20.24,19.96,19.56,526779.81,0.39,1.99,19.436,19.603,19.895,450810.29,478070.87,669042.47 2018-06-08,19.48,19.9,19.39,19.13,498270.41,-0.57,-2.86,19.56,19.501,19.917,455146.43,479343.73,650997.67 2018-06-11,19.21,19.59,19.29,18.91,300834.94,-0.1,-0.52,19.594,19.392,19.938,430935.74,462216.26,637815.81 2018-06-12,19.28,19.82,19.79,19.23,361271.03,0.5,2.59,19.6,19.38,19.944,413385.46,450458.32,615770.25 2018-06-13,19.7,20.27,19.79,19.6,488584.97,0.0,0.0,19.644,19.489,19.976,435148.23,442486.14,609011.37 2018-06-14,19.5,19.78,19.24,19.1,331041.59,-0.25,-1.28,19.5,19.468,19.951,396000.59,423405.44,578528.76 2018-06-15,19.24,19.45,18.81,18.67,338671.03,-0.43,-2.23,19.384,19.472,19.851,364080.71,409613.57,534339.03 2018-06-19,18.37,18.55,16.93,16.93,598508.81,-1.88,-9.99,18.912,19.253,19.635,423615.49,427275.61,498054.07 2018-06-20,16.6,17.21,17.02,16.4,495535.84,0.09,0.53,18.358,18.979,19.411,450468.45,431926.96,480748.76 2018-06-21,17.01,17.71,16.97,16.85,478381.72,-0.05,-0.29,17.794,18.719,19.216,448427.8,441788.02,462705.79 2018-06-22,16.6,17.36,17.35,16.5,370426.56,0.38,2.24,17.416,18.458,19.031,456304.79,426152.69,452111.78 2018-06-25,17.62,17.81,16.92,16.86,417699.44,-0.43,-2.48,17.038,18.211,18.856,472110.47,418095.59,448719.66 2018-06-26,16.51,17.19,17.09,16.34,297902.31,0.17,1.0,17.07,17.991,18.692,411989.17,417802.33,440009.29 2018-06-27,17.13,17.38,16.67,16.54,320759.31,-0.42,-2.46,17.0,17.679,18.53,377033.87,413751.16,432104.74 2018-06-28,16.64,16.97,16.41,16.4,299115.09,-0.26,-1.56,16.888,17.341,18.415,341180.54,394804.17,418645.16 2018-06-29,16.57,17.09,17.08,16.36,387400.25,0.67,4.08,16.834,17.125,18.297,344575.28,400440.04,411922.74 2018-07-02,17.03,17.14,16.8,16.6,337290.25,-0.28,-1.64,16.81,16.924,18.198,328493.44,400301.96,404957.77 2018-07-03,17.27,17.68,17.58,16.92,524702.12,0.78,4.64,16.908,16.989,18.121,373853.4,392921.29,410098.45 2018-07-04,17.51,17.51,17.08,16.95,406939.91,-0.5,-2.84,16.99,16.995,17.987,391089.52,384061.7,407994.33 2018-07-05,17.1,17.22,16.33,16.31,394887.97,-0.75,-4.39,16.974,16.931,17.825,410244.1,375712.32,408750.17 2018-07-06,16.47,16.91,16.6,16.25,394415.38,0.27,1.65,16.878,16.856,17.657,411647.13,378111.2,402131.95 2018-07-09,16.8,17.62,17.61,16.8,532556.0,1.01,6.08,17.04,16.925,17.568,450700.28,389596.86,403846.23 2018-07-10,17.68,18.25,18.0,17.4,596623.94,0.39,2.21,17.124,17.016,17.504,465084.64,419469.02,418635.68 2018-07-11,17.45,17.68,17.53,16.9,498889.84,-0.47,-2.61,17.214,17.102,17.391,483474.63,437282.08,425516.62 2018-07-12,17.49,18.15,18.04,17.38,524338.06,0.51,2.91,17.556,17.265,17.303,509364.64,459804.37,427304.27 2018-07-13,18.0,18.58,18.42,17.88,483554.56,0.38,2.11,17.92,17.399,17.262,527192.48,469419.8,434929.92 2018-07-16,18.38,18.52,18.13,17.95,420261.91,-0.29,-1.57,18.024,17.532,17.228,504733.66,477716.97,439009.46 2018-07-17,18.0,18.04,17.92,17.57,363463.59,-0.21,-1.16,18.008,17.566,17.278,458101.59,461593.12,427257.2 2018-07-18,17.94,18.25,17.73,17.72,366933.25,-0.19,-1.06,18.048,17.631,17.313,431710.27,457592.45,420827.07 2018-07-19,17.7,17.95,17.68,17.4,307490.81,-0.05,-0.28,17.976,17.766,17.349,388340.82,448852.73,412282.53 2018-07-20,17.67,17.99,17.83,17.4,324515.84,0.15,0.85,17.858,17.889,17.373,356533.08,441862.78,409986.99 2018-07-23,17.8,18.43,18.38,17.6,497954.91,0.55,3.08,17.908,17.966,17.446,372071.68,438402.67,413999.77 2018-07-24,18.36,18.9,18.29,18.2,729152.62,-0.09,-0.49,17.982,17.995,17.506,445209.49,451655.54,435562.28 2018-07-25,18.2,18.53,18.35,18.08,365580.12,0.06,0.33,18.106,18.077,17.59,444938.86,438324.57,437803.32 2018-07-26,18.38,18.44,17.9,17.63,540302.38,-0.45,-2.45,18.15,18.063,17.664,491501.17,439921.0,449862.69 2018-07-27,17.71,17.85,17.49,17.36,376730.62,-0.41,-2.29,18.082,17.97,17.685,501944.13,429238.61,449329.2 2018-07-30,17.52,17.75,17.47,17.27,322805.41,-0.02,-0.11,17.9,17.904,17.718,466914.23,419492.96,448604.96 2018-07-31,17.48,17.55,17.5,17.03,242443.48,0.03,0.17,17.742,17.862,17.714,369572.4,407390.94,434492.03 2018-08-01,17.56,17.65,17.17,17.06,313782.25,-0.33,-1.89,17.506,17.806,17.719,359212.83,402075.84,429834.15 2018-08-02,17.02,17.02,16.52,16.19,403556.16,-0.65,-3.79,17.23,17.69,17.728,331863.58,411682.38,430267.56 2018-08-03,16.46,16.61,16.05,16.04,307096.88,-0.47,-2.85,16.942,17.512,17.701,317936.84,409940.48,425901.63 2018-08-06,15.81,16.23,15.31,15.24,376726.75,-0.74,-4.61,16.51,17.205,17.586,328721.1,397817.67,418110.17 2018-08-07,15.54,16.11,16.08,15.32,414727.16,0.77,5.03,16.226,16.984,17.49,363177.84,366375.12,409015.33 2018-08-08,15.99,16.2,15.67,15.63,324346.16,-0.41,-2.55,15.926,16.716,17.397,365290.62,362251.73,400288.15 2018-08-09,15.61,16.33,16.24,15.58,345264.16,0.57,3.64,15.87,16.55,17.307,353632.22,342747.9,391334.45 2018-08-10,16.28,16.55,16.35,16.13,269443.38,0.11,0.68,15.93,16.436,17.203,346101.52,332019.18,380628.89 2018-08-13,16.05,16.45,16.44,15.95,291159.41,0.09,0.55,16.156,16.333,17.119,328988.05,328854.58,374173.77 2018-08-14,16.4,16.88,16.68,16.31,342743.91,0.24,1.46,16.276,16.251,17.057,314591.4,338884.62,373137.78 2018-08-15,16.68,16.7,16.23,16.11,255974.2,-0.45,-2.7,16.388,16.157,16.982,300917.01,333103.82,367589.83 2018-08-16,16.09,16.53,16.17,15.99,284529.09,-0.06,-0.37,16.374,16.122,16.906,288770.0,321201.11,366441.74 2018-08-17,16.48,16.57,15.98,15.92,270309.12,-0.19,-1.18,16.3,16.115,16.814,288943.15,317522.33,363731.41 2018-08-20,15.98,16.47,16.39,15.71,262445.19,0.41,2.57,16.29,16.223,16.714,283200.3,306094.18,351955.92 2018-08-21,16.36,16.63,16.48,16.31,280936.44,0.09,0.55,16.25,16.263,16.624,270838.81,292715.11,329545.11 2018-08-22,16.35,16.48,16.19,16.08,200156.81,-0.29,-1.76,16.242,16.315,16.516,259675.33,280296.17,321273.95 2018-08-23,16.19,16.33,16.24,15.98,196083.23,0.05,0.31,16.256,16.315,16.433,241986.16,265378.08,304062.99 2018-08-24,16.2,16.25,16.05,15.97,161565.08,-0.19,-1.17,16.27,16.285,16.361,220237.35,254590.25,293304.71 2018-08-27,16.1,16.54,16.49,16.07,309079.53,0.44,2.74,16.29,16.29,16.312,229564.22,256382.26,292618.42 2018-08-28,16.52,16.57,16.36,16.3,179477.77,-0.13,-0.79,16.266,16.258,16.255,209272.48,240055.65,289470.13 2018-08-29,16.39,16.39,16.1,16.08,183395.36,-0.26,-1.59,16.248,16.245,16.201,205920.19,232797.76,282950.79 2018-08-30,16.04,16.17,15.86,15.82,174457.2,-0.24,-1.49,16.172,16.214,16.168,201594.99,221790.57,271495.84 2018-08-31,15.88,15.92,15.57,15.57,160528.69,-0.29,-1.83,16.076,16.173,16.144,201387.71,210812.53,264167.43 2018-09-03,15.55,15.58,15.3,14.91,239321.69,-0.27,-1.73,15.838,16.064,16.144,187436.14,208500.18,257297.18 2018-09-04,15.3,15.53,15.42,15.12,164744.02,0.12,0.78,15.65,15.958,16.111,184489.39,196880.94,244798.02 2018-09-05,15.31,15.34,14.9,14.9,205929.48,-0.52,-3.37,15.41,15.829,16.072,188996.22,197458.21,238877.19 2018-09-06,14.8,15.05,14.69,14.63,164705.98,-0.21,-1.41,15.176,15.674,15.995,187045.97,194320.48,229849.28 2018-09-07,14.74,15.21,14.93,14.66,241514.92,0.24,1.63,15.048,15.562,15.924,203243.22,202315.46,228452.86 2018-09-10,14.79,14.88,14.21,14.13,252462.36,-0.72,-4.82,14.83,15.334,15.812,205871.35,196653.75,226518.0 2018-09-11,14.19,14.32,13.41,12.99,548033.75,-0.8,-5.63,14.428,15.039,15.649,282529.3,233509.35,236782.5 2018-09-12,13.51,13.59,13.45,13.3,206414.64,0.04,0.3,14.138,14.774,15.51,282626.33,235811.27,234304.52 2018-09-13,13.66,13.81,13.68,13.41,242011.34,0.23,1.71,13.936,14.556,15.385,298087.4,242566.69,232178.63 2018-09-14,13.69,13.81,13.49,13.37,230403.0,-0.19,-1.39,13.648,14.348,15.261,295865.02,249554.12,230183.32 2018-09-17,13.4,13.46,12.98,12.96,282570.88,-0.51,-3.78,13.402,14.116,15.09,301886.72,253879.04,231189.61 2018-09-18,13.14,13.83,13.7,13.07,333705.12,0.72,5.55,13.46,13.944,14.951,259021.0,270775.15,233828.04 2018-09-19,13.65,13.92,13.68,13.5,347221.75,-0.02,-0.15,13.506,13.822,14.826,287182.42,284904.37,241181.29 2018-09-20,13.71,14.16,13.83,13.71,382658.91,0.15,1.1,13.536,13.736,14.705,315311.93,306699.67,250510.07 2018-09-21,13.84,14.32,14.18,13.66,396101.44,0.35,2.53,13.674,13.661,14.612,348451.62,322158.32,262236.89 2018-09-25,14.01,14.29,14.07,13.92,253864.16,-0.11,-0.78,13.892,13.647,14.491,342710.28,322298.5,259476.12 2018-09-26,14.15,14.65,14.49,14.1,443698.75,0.42,2.98,14.05,13.755,14.397,364709.0,311865.0,272687.17 2018-09-27,14.41,14.49,14.28,14.26,265452.47,-0.21,-1.45,14.17,13.838,14.306,348355.15,317768.78,276790.03 2018-09-28,14.28,14.64,14.6,14.21,281877.69,0.32,2.24,14.324,13.93,14.243,328198.9,321755.42,282161.05 2018-10-08,14.25,14.43,13.91,13.83,310036.47,-0.69,-4.73,14.27,13.972,14.16,310985.91,329718.76,289636.44 2018-10-09,13.99,14.43,14.24,13.98,326492.97,0.33,2.37,14.304,14.098,14.107,325511.67,334110.97,293995.01 2018-10-10,14.35,14.44,14.1,13.96,273177.59,-0.14,-0.98,14.226,14.138,14.041,291407.44,328058.22,299416.68 2018-10-11,12.99,13.37,12.88,12.73,516710.72,-1.22,-8.65,13.946,14.058,13.94,341659.09,345007.12,314955.75 2018-10-12,12.88,12.96,12.8,12.16,415980.91,-0.08,-0.62,13.586,13.955,13.846,368479.73,348339.32,327519.49 2018-10-15,12.92,13.05,12.47,12.41,263047.88,-0.33,-2.58,13.298,13.784,13.723,359082.01,335033.96,328596.14 2018-10-16,12.48,12.62,11.88,11.7,329883.81,-0.59,-4.73,12.826,13.565,13.606,359760.18,342635.93,332467.21 2018-10-17,12.2,12.27,11.95,11.55,289431.78,0.07,0.59,12.396,13.311,13.533,363011.02,327209.23,319537.11 2018-10-18,11.8,11.93,11.4,11.37,272613.56,-0.55,-4.6,12.1,13.023,13.431,314191.59,327925.34,322847.06 2018-10-19,11.1,11.75,11.68,11.02,333744.97,0.28,2.46,11.876,12.731,13.331,297744.4,333112.07,327433.74 2018-10-22,11.79,12.78,12.64,11.78,500358.97,0.96,8.22,11.91,12.604,13.288,345206.62,352144.32,340931.54 2018-10-23,12.61,12.68,12.14,11.99,406853.97,-0.5,-3.96,11.962,12.394,13.246,360600.65,360180.42,347145.69 2018-10-24,12.04,12.25,11.94,11.88,286348.38,-0.2,-1.65,11.96,12.178,13.158,359983.97,361497.5,344777.86 2018-10-25,11.4,11.77,11.7,11.25,318862.91,-0.24,-2.01,12.02,12.06,13.059,369233.84,341712.71,343359.92 2018-10-26,11.89,11.94,11.65,11.59,224693.7,-0.05,-0.43,12.014,11.945,12.95,347423.59,322583.99,335461.66 2018-10-29,11.6,11.64,11.02,10.96,400754.38,-0.63,-5.41,11.69,11.8,12.792,327502.67,336354.64,335694.3 2018-10-30,10.98,11.36,11.23,10.83,310323.0,0.21,1.91,11.508,11.735,12.65,308196.47,334398.56,338517.24 2018-10-31,11.3,11.78,11.6,11.23,389848.84,0.37,3.29,11.44,11.7,12.506,328896.57,344440.27,335824.75 2018-11-01,11.69,11.84,11.56,11.53,390350.69,-0.04,-0.34,11.412,11.716,12.37,343194.12,356213.98,342069.66 2018-11-02,11.79,12.41,12.38,11.69,690194.94,0.82,7.09,11.558,11.786,12.259,436294.37,391858.98,362485.52 2018-11-05,12.3,12.3,12.11,11.93,473343.53,-0.27,-2.18,11.776,11.733,12.169,450812.2,389157.43,370650.88 2018-11-06,12.03,12.08,11.92,11.74,308357.88,-0.19,-1.57,11.914,11.711,12.053,450419.18,379307.83,369744.12 2018-11-07,11.85,12.1,11.86,11.8,338807.25,-0.06,-0.5,11.966,11.703,11.941,440210.86,384553.71,373025.6 2018-11-08,12.02,12.07,11.66,11.6,320866.38,-0.2,-1.69,11.986,11.699,11.88,426314.0,384754.06,363233.39 2018-11-09,11.59,11.63,11.48,11.45,197784.22,-0.18,-1.54,11.806,11.682,11.814,327831.85,382063.11,352323.55 2018-11-12,11.41,11.85,11.85,11.41,279093.59,0.37,3.22,11.754,11.765,11.783,288981.86,369897.03,353125.84 2018-11-13,11.66,11.99,11.88,11.6,376806.97,0.03,0.25,11.746,11.83,11.783,302671.68,376545.43,355472.0 2018-11-14,11.84,12.05,11.8,11.76,347228.09,-0.08,-0.67,11.734,11.85,11.775,304355.85,372283.35,358361.81 2018-11-15,11.76,12.03,12.03,11.7,324807.19,0.23,1.95,11.808,11.897,11.807,305144.01,365729.0,360971.49 2018-11-16,12.05,12.15,12.02,11.91,373166.47,-0.01,-0.08,11.916,11.861,11.824,340220.46,334026.16,362942.57 2018-11-19,12.04,12.28,12.28,11.96,394610.62,0.26,2.16,12.002,11.878,11.806,363323.87,326152.87,357655.15 2018-11-20,12.17,12.18,11.83,11.81,376581.59,-0.45,-3.66,11.992,11.869,11.79,363278.79,332975.24,356141.53 2018-11-21,11.55,11.7,11.66,11.45,296754.47,-0.17,-1.44,11.964,11.849,11.776,353184.07,328769.96,356661.84 2018-11-22,11.69,11.71,11.61,11.54,175418.84,-0.05,-0.43,11.88,11.844,11.772,323306.4,314225.21,349489.63 2018-11-23,11.57,11.6,10.99,10.95,392558.19,-0.62,-5.34,11.674,11.795,11.739,327184.74,333702.6,357882.86 2018-11-26,11.0,11.01,10.72,10.68,269315.91,-0.27,-2.46,11.362,11.682,11.724,302125.8,332724.83,351310.93 2018-11-27,10.8,10.89,10.77,10.69,166765.59,0.05,0.47,11.15,11.571,11.701,260162.6,311720.7,344133.06 2018-11-28,10.78,10.95,10.93,10.61,219351.48,0.16,1.49,11.004,11.484,11.667,244682.0,298933.04,335608.19 2018-11-29,11.0,11.17,10.65,10.63,292247.12,-0.28,-2.56,10.812,11.346,11.622,268047.66,295677.03,330703.02 2018-11-30,10.67,11.05,10.98,10.61,261958.16,0.33,3.1,10.81,11.242,11.552,241927.65,284556.2,309291.18 2018-12-03,11.32,11.53,11.38,11.16,442939.81,0.4,3.64,10.942,11.152,11.515,276652.43,289389.12,307770.99 2018-12-04,11.38,11.48,11.43,11.29,285608.31,0.05,0.44,11.074,11.112,11.491,300420.98,280291.79,306633.51 2018-12-05,11.15,11.4,11.24,11.08,232678.56,-0.19,-1.66,11.136,11.07,11.46,303086.39,273884.2,301327.08 2018-12-06,11.15,11.2,11.02,11.01,210052.05,-0.22,-1.96,11.21,11.011,11.428,286647.38,277347.52,295786.36 2018-12-07,11.06,11.13,11.07,11.01,122096.74,0.05,0.45,11.228,11.019,11.407,258675.09,250301.37,292001.99 2018-12-10,10.95,11.01,10.76,10.76,183989.98,-0.31,-2.8,11.104,11.023,11.353,206885.13,241768.78,287246.81 2018-12-11,10.79,10.87,10.84,10.77,111350.56,0.08,0.74,10.986,11.03,11.301,172033.58,236227.28,273973.99 2018-12-12,10.89,10.96,10.88,10.83,115065.0,0.04,0.37,10.914,11.025,11.255,148510.87,225798.63,262365.83 2018-12-13,10.9,11.26,11.16,10.84,335119.53,0.28,2.57,10.942,11.076,11.211,173524.36,230085.87,262881.45 2018-12-14,11.12,11.18,10.82,10.78,261024.77,-0.34,-3.05,10.892,11.06,11.151,201309.97,229992.53,257274.36 2018-12-17,10.82,10.85,10.79,10.65,177102.86,-0.03,-0.28,10.898,11.001,11.077,199932.54,203408.84,246398.98 2018-12-18,10.69,10.82,10.79,10.61,173313.94,0.0,0.0,10.888,10.937,11.025,212325.22,192179.4,236235.59 2018-12-19,10.78,10.79,10.64,10.6,129787.8,-0.15,-1.39,10.84,10.877,10.974,215269.78,181890.32,227887.26 2018-12-20,10.61,10.78,10.69,10.61,143630.23,0.05,0.47,10.746,10.844,10.928,176971.92,175248.14,226297.83 2018-12-21,10.66,10.66,10.4,10.32,216065.77,-0.29,-2.71,10.662,10.777,10.898,167980.12,184645.04,217473.21 2018-12-24,10.38,10.59,10.51,10.32,121361.64,0.11,1.06,10.606,10.752,10.888,156831.88,178382.21,210075.5 2018-12-25,10.3,10.35,10.3,10.06,232078.78,-0.21,-2.0,10.508,10.698,10.864,168584.84,190455.03,213341.15 2018-12-26,10.26,10.34,10.09,10.09,149200.69,-0.21,-2.04,10.398,10.619,10.822,172467.42,193868.6,209833.62 2018-12-27,10.32,10.36,10.02,10.0,205026.66,-0.07,-0.69,10.264,10.505,10.791,184746.71,180859.31,205472.59 2018-12-28,10.06,10.08,9.8,9.73,240500.77,-0.22,-2.2,10.144,10.403,10.732,189633.71,178806.91,204399.72 2019-01-02,9.87,9.88,9.72,9.68,141343.12,-0.08,-0.82,9.986,10.296,10.649,193630.0,175230.94,189319.89 2019-01-03,9.75,9.92,9.74,9.68,138029.81,0.02,0.21,9.874,10.191,10.564,174820.21,171702.53,181940.96 2019-01-04,9.64,10.13,10.08,9.58,284748.44,0.34,3.49,9.872,10.135,10.506,201929.76,187198.59,184544.46 2019-01-07,10.19,10.19,10.15,10.04,236167.41,0.07,0.69,9.898,10.081,10.463,208157.91,196452.31,185850.23 2019-01-08,10.13,10.13,10.01,10.0,130892.63,-0.14,-1.38,9.94,10.042,10.41,186236.28,187935.0,186290.02 2019-01-09,10.05,10.45,10.15,10.04,390747.97,0.14,1.4,10.026,10.006,10.379,236117.25,214873.63,196627.92 2019-01-10,10.16,10.2,10.07,10.07,193384.98,-0.08,-0.79,10.092,9.983,10.341,247188.29,211004.25,200729.64 2019-01-11,10.07,10.17,10.12,10.04,155181.94,0.05,0.5,10.1,9.986,10.303,221274.99,211602.37,202735.49 2019-01-14,10.11,10.21,10.09,10.04,155330.03,-0.03,-0.3,10.088,9.993,10.249,205107.51,206632.71,193746.01 2019-01-15,10.08,10.35,10.32,10.04,296667.19,0.23,2.28,10.15,10.045,10.224,238262.42,212249.35,195528.13 2019-01-16,10.31,10.42,10.25,10.21,198482.31,-0.07,-0.68,10.17,10.098,10.197,199809.29,217963.27,196597.11 2019-01-17,10.22,10.25,10.14,10.09,162837.69,-0.11,-1.07,10.184,10.138,10.165,193699.83,220444.06,196073.29 2019-01-18,10.16,10.77,10.65,10.15,565978.06,0.51,5.03,10.29,10.195,10.165,275859.06,248567.02,217882.81 2019-01-21,10.63,11.09,11.02,10.58,559175.38,0.37,3.47,10.476,10.282,10.182,356628.13,280867.82,238660.06 2019-01-22,10.94,11.16,11.15,10.76,481678.84,0.13,1.18,10.642,10.396,10.219,393630.46,315946.44,251940.72 2019-01-23,11.01,11.68,11.49,10.95,779547.06,0.34,3.05,10.89,10.53,10.268,509843.41,354826.35,284849.99 2019-01-24,11.36,11.52,11.4,11.29,422766.12,-0.09,-0.78,11.142,10.663,10.323,561829.09,377764.46,294384.36 2019-01-25,11.4,11.64,11.36,11.3,403042.09,-0.04,-0.35,11.284,10.787,10.387,529241.9,402550.48,307076.43 2019-01-28,11.36,11.49,11.4,11.12,374224.28,0.04,0.35,11.36,10.918,10.456,492251.68,424439.9,315536.31 2019-01-29,11.52,11.75,11.65,11.1,605485.19,0.25,2.19,11.46,11.051,10.548,517012.95,455321.7,333785.53 2019-01-30,11.55,11.94,11.6,11.53,468553.69,-0.05,-0.43,11.482,11.186,10.642,454814.27,482328.84,350146.06 2019-01-31,11.7,11.78,11.72,11.53,394720.84,0.12,1.03,11.546,11.344,10.741,449205.22,505517.16,362980.61 2019-02-01,11.82,12.1,12.06,11.71,443738.44,0.34,2.9,11.686,11.485,10.84,457344.49,493293.19,370930.11 ```
用matplotlib作图时设置了x轴主副刻度,怎样旋转副刻度坐标?
用matplotlib作图时设置了x轴主副刻度,用plt.xticks()只能旋转主刻度坐标,怎样旋转副刻度坐标?(副刻度是时分秒) ``` fig,ax = plt.subplots(figsize=(8,3),dpi=128) #把刻度线设置在图的里面 plt.rcParams['xtick.direction'] = 'in' plt.rcParams['ytick.direction'] = 'in' ax.xaxis.set_major_locator(mdates.DayLocator()) ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) #设置x轴副刻度 ax.xaxis.set_minor_locator(mdates.HourLocator(interval=6)) ax.xaxis.set_minor_formatter(mdates.DateFormatter('%H:%M:%S')) ax.plot(jin_data['datetime'],jin_data['data'],linewidth=1,label='进水温度') ax.plot(hui_data['datetime'],hui_data['data'],linewidth=1,label='回水温度') ax.set(xlabel='时间',ylabel='温度(℃)') lengend = ax.legend(loc='best') ax.grid(linestyle='--',linewidth=0.5) plt.tick_params(labelsize=8) # 设置坐标字体大小 plt.xticks(rotation=90) ```![图片说明](https://img-ask.csdn.net/upload/201912/19/1576758870_259924.png) ``` ```
r中~绘图是什么作用。。。。。。。。
绘制箱图的时候 plot(mpg~cylinders,d2),mpg,cylinders是d2的两列,请帮解释下这句用法
R语言图形参数cex.lab没有用
我想用cex.lab缩小plot函数画的图的坐标轴标签的字体大小,但是没有效果。这是为什么,如果这个参数不可行,那还有其他的方法可以实现吗?
130 个相见恨晚的超实用网站,一次性分享出来
相见恨晚的超实用网站 持续更新中。。。
字节跳动视频编解码面经
三四月份投了字节跳动的实习(图形图像岗位),然后hr打电话过来问了一下会不会opengl,c++,shador,当时只会一点c++,其他两个都不会,也就直接被拒了。 七月初内推了字节跳动的提前批,因为内推没有具体的岗位,hr又打电话问要不要考虑一下图形图像岗,我说实习投过这个岗位不合适,不会opengl和shador,然后hr就说秋招更看重基础。我当时想着能进去就不错了,管他哪个岗呢,就同意了面试...
大学四年自学走来,这些私藏的实用工具/学习网站我贡献出来了
大学四年,看课本是不可能一直看课本的了,对于学习,特别是自学,善于搜索网上的一些资源来辅助,还是非常有必要的,下面我就把这几年私藏的各种资源,网站贡献出来给你们。主要有:电子书搜索、实用工具、在线视频学习网站、非视频学习网站、软件下载、面试/求职必备网站。 注意:文中提到的所有资源,文末我都给你整理好了,你们只管拿去,如果觉得不错,转发、分享就是最大的支持了。 一、PDF搜索网站推荐 对于大部
《奇巧淫技》系列-python!!每天早上八点自动发送天气预报邮件到QQ邮箱
此博客仅为我业余记录文章所用,发布到此,仅供网友阅读参考,如有侵权,请通知我,我会删掉。 补充 有不少读者留言说本文章没有用,因为天气预报直接打开手机就可以收到了,为何要多此一举发送到邮箱呢!!!那我在这里只能说:因为你没用,所以你没用!!! 这里主要介绍的是思路,不是天气预报!不是天气预报!!不是天气预报!!!天气预报只是用于举例。请各位不要再刚了!!! 下面是我会用到的两个场景: 每日下
致 Python 初学者
欢迎来到“Python进阶”专栏!来到这里的每一位同学,应该大致上学习了很多 Python 的基础知识,正在努力成长的过程中。在此期间,一定遇到了很多的困惑,对未来的学习方向感到迷茫。我非常理解你们所面临的处境。我从2007年开始接触 python 这门编程语言,从2009年开始单一使用 python 应对所有的开发工作,直至今天。回顾自己的学习过程,也曾经遇到过无数的困难,也曾经迷茫过、困惑过。开办这个专栏,正是为了帮助像我当年一样困惑的 Python 初学者走出困境、快速成长。希望我的经验能真正帮到你
加快推动区块链技术和产业创新发展,2019可信区块链峰会在京召开
      11月8日,由中国信息通信研究院、中国通信标准化协会、中国互联网协会、可信区块链推进计划联合主办,科技行者协办的2019可信区块链峰会将在北京悠唐皇冠假日酒店开幕。   区块链技术被认为是继蒸汽机、电力、互联网之后,下一代颠覆性的核心技术。如果说蒸汽机释放了人类的生产力,电力解决了人类基本的生活需求,互联网彻底改变了信息传递的方式,区块链作为构造信任的技术有重要的价值。   1
8年经验面试官详解 Java 面试秘诀
    作者 | 胡书敏 责编 | 刘静 出品 | CSDN(ID:CSDNnews) 本人目前在一家知名外企担任架构师,而且最近八年来,在多家外企和互联网公司担任Java技术面试官,前后累计面试了有两三百位候选人。在本文里,就将结合本人的面试经验,针对Java初学者、Java初级开发和Java开发,给出若干准备简历和准备面试的建议。   Java程序员准备和投递简历的实
知乎高赞:中国有什么拿得出手的开源软件产品?(整理自本人原创回答)
知乎高赞:中国有什么拿得出手的开源软件产品? 在知乎上,有个问题问“中国有什么拿得出手的开源软件产品(在 GitHub 等社区受欢迎度较好的)?” 事实上,还不少呢~ 本人于2019.7.6进行了较为全面的 回答 - Bravo Yeung,获得该问题下回答中得最高赞(236赞和1枚专业勋章),对这些受欢迎的 Github 开源项目分类整理如下: 分布式计算、云平台相关工具类 1.SkyWalk
iOS Bug 太多,苹果终于坐不住了!
开源的 Android 和闭源的 iOS,作为用户的你,更偏向哪一个呢? 整理 | 屠敏 出品 | CSDN(ID:CSDNnews) 毋庸置疑,当前移动设备操作系统市场中,Android 和 iOS 作为两大阵营,在相互竞争的同时不断演进。不过一直以来,开源的 Android 吸引了无数的手机厂商涌入其中,为其生态带来了百花齐放的盛景,但和神秘且闭源的 iOS 系统相比,不少网友
究竟你适不适合买Mac?
我清晰的记得,刚买的macbook pro回到家,开机后第一件事情,就是上了淘宝网,花了500元钱,找了一个上门维修电脑的师傅,上门给我装了一个windows系统。。。。。。 表砍我。。。 当时买mac的初衷,只是想要个固态硬盘的笔记本,用来运行一些复杂的扑克软件。而看了当时所有的SSD笔记本后,最终决定,还是买个好(xiong)看(da)的。 已经有好几个朋友问我mba怎么样了,所以今天尽量客观...
为什么你学不过动态规划?告别动态规划,谈谈我的经验
动态规划难吗?说实话,我觉得很难,特别是对于初学者来说,我当时入门动态规划的时候,是看 0-1 背包问题,当时真的是一脸懵逼。后来,我遇到动态规划的题,看的懂答案,但就是自己不会做,不知道怎么下手。就像做递归的题,看的懂答案,但下不了手,关于递归的,我之前也写过一篇套路的文章,如果对递归不大懂的,强烈建议看一看:为什么你学不会递归,告别递归,谈谈我的经验 对于动态规划,春招秋招时好多题都会用到动态...
(经验分享)作为一名普通本科计算机专业学生,我大学四年到底走了多少弯路
今年正式步入了大四,离毕业也只剩半年多的时间,回想一下大学四年,感觉自己走了不少弯路,今天就来分享一下自己大学的学习经历,也希望其他人能不要走我走错的路。 (一)初进校园 刚进入大学的时候自己完全就相信了高中老师的话:“进入大学你们就轻松了”。因此在大一的时候自己学习的激情早就被抛地一干二净,每天不是在寝室里玩游戏就是出门游玩,不过好在自己大学时买的第一台笔记本性能并不是很好,也没让我彻底沉...
使用 Angular 打造微前端架构的 ToB 企业级应用
这篇文章其实已经准备了11个月了,因为虽然我们年初就开始使用 Angular 的微前端架构,但是产品一直没有正式发布,无法通过生产环境实践验证可行性,11月16日我们的产品正式灰度发布,所以是时候分享一下我们在使用 Angular 微前端这条路上的心得(踩过的坑)了额,希望和 Angular 社区一起成长一起进步,如果你对微前端有一定的了解并且已经在项目中尝试了可以忽略前面的章节。 什么是微前...
大学四年因为知道了这32个网站,我成了别人眼中的大神!
依稀记得,毕业那天,我们导员发给我毕业证的时候对我说“你可是咱们系的风云人物啊”,哎呀,别提当时多开心啦????,嗯,我们导员是所有导员中最帅的一个,真的???? 不过,导员说的是实话,很多人都叫我大神的,为啥,因为我知道这32个网站啊,你说强不强????,这次是绝对的干货,看好啦,走起来! PS:每个网站都是学计算机混互联网必须知道的,真的牛杯,我就不过多介绍了,大家自行探索,觉得没用的,尽管留言吐槽吧???? 社...
拿下微软、Google、Adobe,印度为何盛产科技圈 CEO?
作者 | 胡巍巍 出品 | CSDN(ID:CSDNnews) 世界500强中,30%的掌舵人,都是印度人。 是的,你没看错。这是近日《哈佛商业评论》的研究结果。 其中又以微软CEO萨提亚·纳德拉(Satya Nadella)、和谷歌CEO桑达尔·皮查伊(Sundar Pichai,以下简称劈柴)最为出名。 微软CEO萨提亚·纳德拉(Satya Nadella) 其他著名印度...
程序员写了一个新手都写不出的低级bug,被骂惨了。
这种新手都不会范的错,居然被一个工作好几年的小伙子写出来,差点被当场开除了。
Java工作4年来应聘要16K最后没要,细节如下。。。
前奏: 今天2B哥和大家分享一位前几天面试的一位应聘者,工作4年26岁,统招本科。 以下就是他的简历和面试情况。 基本情况: 专业技能: 1、&nbsp;熟悉Sping了解SpringMVC、SpringBoot、Mybatis等框架、了解SpringCloud微服务 2、&nbsp;熟悉常用项目管理工具:SVN、GIT、MAVEN、Jenkins 3、&nbsp;熟悉Nginx、tomca...
一文带你入门Linux
文章目录1.1 Linux的概述:1.1.1 什么是Linux:1.1.1.1 学习Linux之前先了解Unix1.1.1.2 Linux的概述:1.1.1.3 Linux的历史:1.1.1.4 Linux系统的应用:1.1.1.5 Linux的版本1.1.1.6 Linux的主流版本1.2 Linux的安装:1.2.1 虚拟机安装:1.2.1.1 什么是虚拟机1.2.1.2 安装VmWare1....
普通三本毕业,我怎么一路艰辛进入阿里的
英雄不问出处? 自古以来就有这样一句话,真的英雄不问出处吗?这句话太挫了。普通三本院校的我,大四的时候居然都不知道什么是校招,所以出处太重要了。这也是没有机会参加阿里校招的原因,毕竟校招门槛比社招还是要低的,最重要的是校招进入阿里能让你的起点比别人更高。 有幸可以社招进入阿里,了解了校招的思路,赶紧介绍给学弟们,现在我们三本院校的小学弟今年居然有 3 个人通过了阿里的校招。下面我也把这份宝贵的经...
作为一个程序员,CPU的这些硬核知识你必须会!
CPU对每个程序员来说,是个既熟悉又陌生的东西? 如果你只知道CPU是中央处理器的话,那可能对你并没有什么用,那么作为程序员的我们,必须要搞懂的就是CPU这家伙是如何运行的,尤其要搞懂它里面的寄存器是怎么一回事,因为这将让你从底层明白程序的运行机制。 随我一起,来好好认识下CPU这货吧 把CPU掰开来看 对于CPU来说,我们首先就要搞明白它是怎么回事,也就是它的内部构造,当然,CPU那么牛的一个东...
破14亿,Python分析我国存在哪些人口危机!
一、背景 二、爬取数据 三、数据分析 1、总人口 2、男女人口比例 3、人口城镇化 4、人口增长率 5、人口老化(抚养比) 6、各省人口 7、世界人口 四、遇到的问题 遇到的问题 1、数据分页,需要获取从1949-2018年数据,观察到有近20年参数:LAST20,由此推测获取近70年的参数可设置为:LAST70 2、2019年数据没有放上去,可以手动添加上去 3、将数据进行 行列转换 4、列名...
强烈推荐10本程序员在家读的书
很遗憾,这个春节注定是刻骨铭心的,新型冠状病毒让每个人的神经都是紧绷的。那些处在武汉的白衣天使们,尤其值得我们的尊敬。而我们这些窝在家里的程序员,能不外出就不外出,就是对社会做出的最大的贡献。 有些读者私下问我,窝了几天,有点颓丧,能否推荐几本书在家里看看。我花了一天的时间,挑选了 10 本我最喜欢的书,你可以挑选感兴趣的来读一读。读书不仅可以平复恐惧的压力,还可以对未来充满希望,毕竟苦难终将会...
Python实战:抓肺炎疫情实时数据,画2019-nCoV疫情地图
今天,群里白垩老师问如何用python画武汉肺炎疫情地图。白垩老师是研究海洋生态与地球生物的学者,国家重点实验室成员,于不惑之年学习python,实为我等学习楷模。先前我并没有关注武汉肺炎的具体数据,也没有画过类似的数据分布图。于是就拿了两个小时,专门研究了一下,遂成此文。
关于2020年个人项目【臻美_疫情实时大数据报告】(项目开源)
本项目开源,供大家学习交流,数据来自官方通报。 项目网址: 点这可以查看项目 项目图例: 1、国内疫情(省) 2、国内疫情(市) 3、国外疫情 4、热点消息、辟谣消息 5、防疫知识 源码奉上: 本项目后台使用node.js app.js var originRequest = require('request'); var iconv = require('iconv-lite'...
[数据结构与算法] 排序算法
终于学习到了算法部分, 在学习算法时, 我们还是应该回顾一下数据结构与算法之间的关系 数据结构是研究数据的组织方式, 是算法的基础 算法是解决编程问题的方法论, 是程序的灵魂 程序= 数据结构+算法 排序算法 排序也称排序算法(Sort algorithm). 是指 将一组数据按照指定顺序进行排列的过程 主要分为内部排序和外部排序 内部排序: 指将需要处理的数据加载到内存中进行排序 外部排序...
听说想当黑客的都玩过这个Monyer游戏(1~14攻略)
第零关 进入传送门开始第0关(游戏链接) 请点击链接进入第1关: 连接在左边→ ←连接在右边 看不到啊。。。。(只能看到一堆大佬做完的留名,也能看到菜鸡的我,在后面~~) 直接fn+f12吧 &lt;span&gt;连接在左边→&lt;/span&gt; &lt;a href="first.php"&gt;&lt;/a&gt; &lt;span&gt;←连接在右边&lt;/span&gt; o...
智力题(程序员面试经典)
NO.1  有20瓶药丸,其中19瓶装有1克/粒的药丸,余下一瓶装有1.1克/粒的药丸。给你一台称重精准的天平,怎么找出比较重的那瓶药丸?天平只能用一次。 解法 有时候,严格的限制条件有可能反倒是解题的线索。在这个问题中,限制条件是天平只能用一次。 因为天平只能用一次,我们也得以知道一个有趣的事实:一次必须同时称很多药丸,其实更准确地说,是必须从19瓶拿出药丸进行称重。否则,如果跳过两瓶或更多瓶药...
自学python网络爬虫,从小白快速成长,分别实现静态网页爬取,下载meiztu中图片;动态网页爬取,下载burberry官网所有当季新品图片。
文章目录 1.前言 2.知识储备 3.爬取静态网站 4.爬取动态网站 1.前言 近日疫情严重,手机已经玩吐了,闲着无聊逛衣服品牌官网发现,结果一时兴起,想学一学python,写一个爬虫下载官网所有最新上架的衣服图片和价格;说干就干,但身为一个只学过一些c和c++的python 0基础大二小白,csdn上的各种教程里涉及的各种发法、工具和库让我眼花缭乱;因此走了很多弯路,终于花三天时间完成了爬虫的设...
面试官问你MyBatis SQL是如何执行的?把这篇文章甩给他
初识 MyBatis MyBatis 是第一个支持自定义 SQL、存储过程和高级映射的类持久框架。MyBatis 消除了大部分 JDBC 的样板代码、手动设置参数以及检索结果。MyBatis 能够支持简单的 XML 和注解配置规则。使 Map 接口和 POJO 类映射到数据库字段和记录。 MyBatis 的特点 那么 MyBatis 具有什么特点呢?或许我们可以从如下几个方面来描述 MyBati...
对Tomcat的简单概要小结
首先我们必须得知道Tomcat就是一个服务,一个本地服务,我们可以控制启动和停止,我们程序员通过这个服务主要是用来存放我们的java程序,当我们把Java程序放进Tomcat服务中,一旦Tomcat服务启动起来,其他电脑就可以进行网络连通,也就是说其他电脑也可以共同访问这个Java程序。 Tomcat的主要目录的概念 有上面的概念之后,我们再来知道一下tomcat根目录下都有哪些文件,以及这些文...
程序员回家过年,外婆说没带女朋友别回来了?喝了老爸89年的酒,当场反目。
点赞再看,养成习惯,微信搜索【三太子敖丙】关注这个被微信官方推荐过的逗比 本文 GitHub https://github.com/JavaFamily 已收录,有一线大厂面试点思维导图,也整理了很多我的文档,欢迎Star和完善,大家面试可以参照考点系统复习,希望我们一起有点东西。 注:本文是水日常的文章,不是技术文,看技术的同学可以右划了。 今天是除夕,帅丙也不水太多东西了,先祝大家...
相关热词 c# 压缩图片好麻烦 c#计算数组中的平均值 c#获取路由参数 c#日期精确到分钟 c#自定义异常必须继承 c#查表并返回值 c# 动态 表达式树 c# 监控方法耗时 c# listbox c#chart显示滚动条
立即提问